
# Importando as bibliotecas
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split


# Adicionando o conjunto de dados
dados = pd.read_table('german.txt', sep=' ')

# Adicionando o nome das colunas
dados.columns = ['checking_account', 'duration_month', 'credit_history', 'purpose', 'credit_amount', 'savings_account', 'employed_since', 'installment_rate', 'status_sex', 'other_debtors', 'residence_since', 'property', 'age', 'other_installment', 'housing', 'number_existing_credits', 'job', 'number_people_provide_maintenance', 'telephone', 'foreign_worker', 'response_variable']

display(dados)


# Analisando o tipo de dados de cada coluna
dados.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 999 entries, 0 to 998
Data columns (total 21 columns):
 #   Column                             Non-Null Count  Dtype 
---  ------                             --------------  ----- 
 0   checking_account                   999 non-null    object
 1   duration_month                     999 non-null    int64 
 2   credit_history                     999 non-null    object
 3   purpose                            999 non-null    object
 4   credit_amount                      999 non-null    int64 
 5   savings_account                    999 non-null    object
 6   employed_since                     999 non-null    object
 7   installment_rate                   999 non-null    int64 
 8   status_sex                         999 non-null    object
 9   other_debtors                      999 non-null    object
 10  residence_since                    999 non-null    int64 
 11  property                           999 non-null    object
 12  age                                999 non-null    int64 
 13  other_installment                  999 non-null    object
 14  housing                            999 non-null    object
 15  number_existing_credits            999 non-null    int64 
 16  job                                999 non-null    object
 17  number_people_provide_maintenance  999 non-null    int64 
 18  telephone                          999 non-null    object
 19  foreign_worker                     999 non-null    object
 20  response_variable                  999 non-null    int64 
dtypes: int64(8), object(13)
memory usage: 164.0+ KB


# Analisando os dados com pandas profiling
from ipywidgets import HTML, Button, widgets  #pip install ipywidgets
from pandas_profiling.report.presentation.core import Alerts
from pandas_profiling.report.presentation.flavours.html import templates

profile = ProfileReport(dados)

profile.to_file('analise_exploratoria.html')

profile


# separando os dataframes para transformar em dummies (conversão de variáveis categóricas para número, sem considerar pesos númericos)

# célula executada apenas uma vez, se não deleta a coluna 'response_variable' e teríamos que importar e renomear novamente o arquivo

# Caso precise, só descomentar as linhas abaixo para repetir o processo.

dados = pd.read_table('german.txt', sep=' ')

dados.columns = ['checking_account', 'duration_month', 'credit_history', 'purpose', 'credit_amount', 'savings_account', 'employed_since', 'installment_rate', 'status_sex', 'other_debtors', 'residence since', 'property', 'age', 'other_installment', 'housing', 'number_existing_credits', 'job', 'number_people_provide_maintenance', 'telephone', 'foreign_worker', 'response_variable']

dados_y = dados['response_variable']
dados_x = dados.drop(dados.columns[-1], axis=1)

print(dados_x)

    checking_account  duration_month credit_history purpose  credit_amount  \
0                A12              48            A32     A43           5951   
1                A14              12            A34     A46           2096   
2                A11              42            A32     A42           7882   
3                A11              24            A33     A40           4870   
4                A14              36            A32     A46           9055   
..               ...             ...            ...     ...            ...   
994              A14              12            A32     A42           1736   
995              A11              30            A32     A41           3857   
996              A14              12            A32     A43            804   
997              A11              45            A32     A43           1845   
998              A12              45            A34     A41           4576   

    savings_account employed_since  installment_rate status_sex other_debtors  \
0               A61            A73                 2        A92          A101   
1               A61            A74                 2        A93          A101   
2               A61            A74                 2        A93          A103   
3               A61            A73                 3        A93          A101   
4               A65            A73                 2        A93          A101   
..              ...            ...               ...        ...           ...   
994             A61            A74                 3        A92          A101   
995             A61            A73                 4        A91          A101   
996             A61            A75                 4        A93          A101   
997             A61            A73                 4        A93          A101   
998             A62            A71                 3        A93          A101   

     residence since property  age other_installment housing  \
0                  2     A121   22              A143    A152   
1                  3     A121   49              A143    A152   
2                  4     A122   45              A143    A153   
3                  4     A124   53              A143    A153   
4                  4     A124   35              A143    A153   
..               ...      ...  ...               ...     ...   
994                4     A121   31              A143    A152   
995                4     A122   40              A143    A152   
996                4     A123   38              A143    A152   
997                4     A124   23              A143    A153   
998                4     A123   27              A143    A152   

     number_existing_credits   job  number_people_provide_maintenance  \
0                          1  A173                                  1   
1                          1  A172                                  2   
2                          1  A173                                  2   
3                          2  A173                                  2   
4                          1  A172                                  2   
..                       ...   ...                                ...   
994                        1  A172                                  1   
995                        1  A174                                  1   
996                        1  A173                                  1   
997                        1  A173                                  1   
998                        1  A173                                  1   

    telephone foreign_worker  
0        A191           A201  
1        A191           A201  
2        A191           A201  
3        A191           A201  
4        A192           A201  
..        ...            ...  
994      A191           A201  
995      A192           A201  
996      A191           A201  
997      A192           A201  
998      A191           A201  

[999 rows x 20 columns]


# Convertendo os valores categoricos em numeros para usar no modelo de regressão.

# Conversão para visualizarmos o pandas profiling

dados_dummies = pd.get_dummies(dados)

profile = ProfileReport(dados_dummies)

profile


# Convertendo valores de string para valores númericos para conseguirmos usar no modelo de Regressão.
dummies = pd.get_dummies(dados_x)

print(dummies.columns)

print(dummies)

Index(['duration_month', 'credit_amount', 'installment_rate',
       'residence since', 'age', 'number_existing_credits',
       'number_people_provide_maintenance', 'checking_account_A11',
       'checking_account_A12', 'checking_account_A13', 'checking_account_A14',
       'credit_history_A30', 'credit_history_A31', 'credit_history_A32',
       'credit_history_A33', 'credit_history_A34', 'purpose_A40',
       'purpose_A41', 'purpose_A410', 'purpose_A42', 'purpose_A43',
       'purpose_A44', 'purpose_A45', 'purpose_A46', 'purpose_A48',
       'purpose_A49', 'savings_account_A61', 'savings_account_A62',
       'savings_account_A63', 'savings_account_A64', 'savings_account_A65',
       'employed_since_A71', 'employed_since_A72', 'employed_since_A73',
       'employed_since_A74', 'employed_since_A75', 'status_sex_A91',
       'status_sex_A92', 'status_sex_A93', 'status_sex_A94',
       'other_debtors_A101', 'other_debtors_A102', 'other_debtors_A103',
       'property_A121', 'property_A122', 'property_A123', 'property_A124',
       'other_installment_A141', 'other_installment_A142',
       'other_installment_A143', 'housing_A151', 'housing_A152',
       'housing_A153', 'job_A171', 'job_A172', 'job_A173', 'job_A174',
       'telephone_A191', 'telephone_A192', 'foreign_worker_A201',
       'foreign_worker_A202'],
      dtype='object')
     duration_month  credit_amount  installment_rate  residence since  age  \
0                48           5951                 2                2   22   
1                12           2096                 2                3   49   
2                42           7882                 2                4   45   
3                24           4870                 3                4   53   
4                36           9055                 2                4   35   
..              ...            ...               ...              ...  ...   
994              12           1736                 3                4   31   
995              30           3857                 4                4   40   
996              12            804                 4                4   38   
997              45           1845                 4                4   23   
998              45           4576                 3                4   27   

     number_existing_credits  number_people_provide_maintenance  \
0                          1                                  1   
1                          1                                  2   
2                          1                                  2   
3                          2                                  2   
4                          1                                  2   
..                       ...                                ...   
994                        1                                  1   
995                        1                                  1   
996                        1                                  1   
997                        1                                  1   
998                        1                                  1   

     checking_account_A11  checking_account_A12  checking_account_A13  ...  \
0                       0                     1                     0  ...   
1                       0                     0                     0  ...   
2                       1                     0                     0  ...   
3                       1                     0                     0  ...   
4                       0                     0                     0  ...   
..                    ...                   ...                   ...  ...   
994                     0                     0                     0  ...   
995                     1                     0                     0  ...   
996                     0                     0                     0  ...   
997                     1                     0                     0  ...   
998                     0                     1                     0  ...   

     housing_A152  housing_A153  job_A171  job_A172  job_A173  job_A174  \
0               1             0         0         0         1         0   
1               1             0         0         1         0         0   
2               0             1         0         0         1         0   
3               0             1         0         0         1         0   
4               0             1         0         1         0         0   
..            ...           ...       ...       ...       ...       ...   
994             1             0         0         1         0         0   
995             1             0         0         0         0         1   
996             1             0         0         0         1         0   
997             0             1         0         0         1         0   
998             1             0         0         0         1         0   

     telephone_A191  telephone_A192  foreign_worker_A201  foreign_worker_A202  
0                 1               0                    1                    0  
1                 1               0                    1                    0  
2                 1               0                    1                    0  
3                 1               0                    1                    0  
4                 0               1                    1                    0  
..              ...             ...                  ...                  ...  
994               1               0                    1                    0  
995               0               1                    1                    0  
996               1               0                    1                    0  
997               0               1                    1                    0  
998               1               0                    1                    0  

[999 rows x 61 columns]


# Carregando os dados em um array numpy
X = np.array(dummies.values)
y = np.array(dados_y.values)

print(X)
print(X.shape)
print(y)
print(y.shape)

[[  48 5951    2 ...    0    1    0]
 [  12 2096    2 ...    0    1    0]
 [  42 7882    2 ...    0    1    0]
 ...
 [  12  804    4 ...    0    1    0]
 [  45 1845    4 ...    1    1    0]
 [  45 4576    3 ...    0    1    0]]
(999, 61)
[2 1 1 2 1 1 1 1 2 2 2 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 2
 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 2 2 1 1 1 1 2 1 1 1 1 1 2
 1 2 1 1 1 2 1 1 1 1 1 1 2 1 2 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1
 1 2 1 1 2 1 2 1 2 1 1 1 2 1 1 2 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1
 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 2 1 2 2 1 1 1 1 2 2 2 1 2 1
 2 1 2 1 2 2 2 1 2 2 1 2 1 2 1 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1
 1 1 1 2 2 2 1 2 1 1 1 1 2 2 2 1 1 2 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 2 1 1
 1 1 1 1 1 2 1 1 2 1 1 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 2 1
 1 1 1 1 2 2 1 2 1 1 2 2 1 1 1 1 2 1 2 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 2
 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 2 1 2 1 2 1 1 1 1 2 1 1 1 2 1 1
 1 1 1 2 2 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 2 1 1
 1 2 1 1 2 1 2 1 2 1 1 2 1 1 1 1 2 1 1 1 1 2 1 2 1 1 1 2 1 1 1 2 1 1 1 2 2
 1 2 1 1 2 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 1 2 2 1 1 1 1 1 1
 1 1 1 2 1 1 1 1 1 2 1 1 1 2 2 1 1 1 2 1 1 2 2 2 1 2 1 1 2 1 1 1 1 1 1 2 1
 1 1 2 2 1 1 1 1 1 2 1 1 2 1 1 1 2 1 1 2 1 2 1 2 2 1 2 1 1 2 1 1 1 2 1 1 2
 2 2 2 2 1 2 1 2 1 1 2 1 1 2 2 1 1 1 1 1 1 1 2 1 2 1 1 2 1 2 1 1 2 2 1 1 1
 2 2 2 2 2 2 1 1 2 2 2 1 1 1 2 1 1 2 2 1 1 2 1 1 1 2 1 1 2 2 1 2 1 1 2 1 1
 1 2 1 2 2 1 1 1 1 2 2 1 2 1 1 2 1 2 2 2 1 2 2 2 1 1 2 1 1 1 1 2 1 1 1 1 1
 1 2 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1
 1 1 2 2 1 1 1 2 1 1 2 1 1 1 1 1 2 2 2 1 2 1 1 2 2 1 1 2 1 1 1 1 2 1 1 2 1
 1 1 1 1 1 1 2 1 1 1 2 1 1 2 2 1 2 1 2 1 2 1 2 1 1 2 1 1 1 1 2 1 1 1 2 1 1
 1 1 2 1 1 2 1 1 1 1 2 2 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 2 2 2
 1 1 1 1 2 1 1 2 1 1 1 2 2 2 1 1 2 2 1 2 2 1 1 1 1 2 1 2 1 1 1 2 1 1 2 2 1
 1 2 1 1 1 1 2 1 1 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 1 2 1
 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 2 2 2 1 1 2 1 2 2
 1 2 1 1 1 2 1 1 1 2 2 1 2 1 1 1 1 1 1 1 2 1 2 2 1 2 2 2 1 1 1 1 2 1 1 1 1
 2 1 1 2 1 1 1 1 1 2 2 1 1 1 1 2 2 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1]
(999,)


# normalizando e padronizando os dados
# MinMaxScaler é usado para normalizar as variáveis, e StandardScaler é usado para padronizar
from sklearn.preprocessing import MinMaxScaler, StandardScaler

# dados x (features) normalizados
# X = np.array(X)


# normalizando
scaler = MinMaxScaler()
scaler.fit(X)
normalized_data = scaler.transform(X)
print(normalized_data)

# Padronizando
scaler = StandardScaler()
scaler.fit(X)
standardized_data = scaler.transform(X)
print(standardized_data)

print(standardized_data.shape)

X = standardized_data

[[0.64705882 0.31368989 0.33333333 ... 0.         1.         0.        ]
 [0.11764706 0.10157368 0.33333333 ... 0.         1.         0.        ]
 [0.55882353 0.41994057 0.33333333 ... 0.         1.         0.        ]
 ...
 [0.11764706 0.03048311 1.         ... 0.         1.         0.        ]
 [0.60294118 0.08776274 1.         ... 1.         1.         0.        ]
 [0.60294118 0.23803235 0.66666667 ... 0.         1.         0.        ]]
[[ 2.24755338  0.94885997 -0.86919627 ... -0.8222983   0.19611614
  -0.19611614]
 [-0.74010176 -0.41721553 -0.86919627 ... -0.8222983   0.19611614
  -0.19611614]
 [ 1.74961086  1.633138   -0.86919627 ... -0.8222983   0.19611614
  -0.19611614]
 ...
 [-0.74010176 -0.87505459  0.91932499 ... -0.8222983   0.19611614
  -0.19611614]
 [ 1.99858212 -0.50616105  0.91932499 ...  1.21610369  0.19611614
  -0.19611614]
 [ 1.99858212  0.46160866  0.02506436 ... -0.8222983   0.19611614
  -0.19611614]]
(999, 61)


# Dividir os dados em conjunto de treinamento e teste
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=45)


# Criando o modelo
model = LogisticRegression(random_state=0,max_iter=1000)

# Treinando o modelo
model.fit(X_train, y_train)

clf2 = LogisticRegression(random_state=45,max_iter=1000).fit(X_train, y_train)


# Fazendo a previsão das classes
y_pred2 = clf2.predict(X_test)


# Avaliando o erro

from sklearn.metrics import confusion_matrix

confusion_matrix(y_test,y_pred2)

# Identificando Verdadeiros positivos (185), falso negativo (19), falso positivo (54) e verdadeiro negativo (42)

array([[185,  19],
       [ 54,  42]], dtype=int64)


# Avaliando o modelo 
# score = model.score(X_test, y_test)

from sklearn import metrics

score = metrics.accuracy_score(y_test, y_pred2)

print('Acurácia:', score)

# Percentagem de acerto

Acurácia: 0.7566666666666667


# Usando o modelo para previsão
predictions = model.predict(X_test)
print(predictions)

[1 1 2 1 1 2 1 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 2 1 1 1 2
 2 1 2 1 1 1 1 1 2 2 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1
 1 1 1 1 1 2 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 2
 1 1 1 1 1 2 2 1 2 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1
 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 2 1 2 1 1 2 1 1 1
 1 1 2 1 1 1 2 2 2 1 1 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 2 1 1
 2 1 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1]


import matplotlib.pyplot as plt
from scipy.special import expit

x = np.linspace(X_train.min(), X_train.max(), 100)
y = expit(x)
plt.plot(x, y)
plt.grid()
plt.xlim(X_train.min(), X_train.max())
plt.xlabel('x')
plt.title('expit(x)')
plt.show()


# Comparando a previsão com o valor real
print('O valor de y teste é:')
print(y_test)

print('O valor do y_pred é:')
print(predictions)

O valor de y teste é:
[2 1 1 1 2 2 1 2 1 1 1 2 1 2 1 1 2 1 2 1 2 1 1 1 1 1 1 2 1 2 2 1 2 1 1 1 2
 1 1 2 1 1 1 1 1 2 2 1 1 2 2 1 1 1 2 2 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 2 1
 1 2 1 1 1 2 2 1 1 1 1 1 1 2 1 1 2 1 2 2 1 1 2 1 1 1 2 1 2 1 1 1 2 1 2 1 1
 1 1 1 1 1 2 2 1 2 2 1 1 1 1 1 2 1 2 2 1 1 1 2 2 1 1 2 1 1 2 2 1 2 1 1 1 1
 1 2 2 2 2 1 2 1 1 2 1 1 1 1 1 1 2 2 1 1 1 1 1 1 2 1 1 1 2 1 2 1 1 1 1 1 1
 1 2 2 1 1 1 2 2 1 2 1 2 2 2 2 2 1 1 1 1 2 2 2 1 1 2 1 1 1 1 2 2 2 1 2 1 1
 1 1 2 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 2
 1 1 1 1 1 2 1 1 1 2 1 2 2 1 2 1 1 1 2 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1
 1 2 1 1]
O valor do y_pred é:
[1 1 2 1 1 2 1 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 2 1 1 1 2
 2 1 2 1 1 1 1 1 2 2 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1
 1 1 1 1 1 2 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 2
 1 1 1 1 1 2 2 1 2 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1
 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 2 1 2 1 1 2 1 1 1
 1 1 2 1 1 1 2 2 2 1 1 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 2 1 1
 2 1 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1]


# Fazendo a previsão das probabilidades
proba = clf2.predict_proba(X_test)

# Probabilidade de ser um bom pagador (índice 1-- quando y é 2.)
probabilidade_bom_pagador = proba[:,1]

list(probabilidade_bom_pagador)

[0.3575544075252283,
 0.24646200250250866,
 0.5037693260003645,
 0.02497158281026338,
 0.4082282634557703,
 0.8396373573662871,
 0.03100479224973886,
 0.5643333501182854,
 0.4242181473053391,
 0.11741470925752992,
 0.017331513227658395,
 0.11632039581080272,
 0.08397560168187373,
 0.7561927547858469,
 0.7135583243294291,
 0.011459321688212348,
 0.4014154578585699,
 0.07180626182376008,
 0.22654904845058418,
 0.19910456663209714,
 0.4358859682904516,
 0.16064706315827837,
 0.06315308004853747,
 0.03921624763197009,
 0.23000329405919429,
 0.03727776191165742,
 0.23763521504374635,
 0.2765074985914869,
 0.12548629380847817,
 0.5835094740206213,
 0.7173070421756433,
 0.2591954913211902,
 0.577429168385609,
 0.30701291653610324,
 0.20932621370528703,
 0.021161840340502242,
 0.6104823106627675,
 0.9276146170465203,
 0.02483958659114614,
 0.7512586796134002,
 0.09271098278817295,
 0.23693867362439494,
 0.11671643310558112,
 0.21428413633601154,
 0.4437466419840958,
 0.6611530329661964,
 0.8724020228133617,
 0.06705332734274236,
 0.014576300226733965,
 0.6216404764826171,
 0.6910763836788373,
 0.03674269300011206,
 0.17662300884992477,
 0.3466366269818069,
 0.8319921175105611,
 0.24745715585768513,
 0.3422182546227761,
 0.3381095803910675,
 0.045855326119499,
 0.0008618225770872446,
 0.260000674389578,
 0.3165078372983348,
 0.4541760632140915,
 0.36362224281308475,
 0.040390012170572295,
 0.3328964934279847,
 0.3853627257694505,
 0.17377068023274117,
 0.01671335107243509,
 0.6676923364403986,
 0.2896107050583962,
 0.02816177160420736,
 0.8271407228955002,
 0.10922638168558309,
 0.16579693885419425,
 0.38213303393089015,
 0.3693948972274035,
 0.30889087725557884,
 0.15099838712559335,
 0.6394255082516275,
 0.49918128312109455,
 0.08527156332759557,
 0.023441448908468797,
 0.537347570171199,
 0.535410661426317,
 0.007368037565589074,
 0.16748460224666506,
 0.14662769993138539,
 0.07609181146385095,
 0.1033535251959754,
 0.11008377363372224,
 0.06591867639250963,
 0.4147241203205284,
 0.28265565343146853,
 0.2676712936290625,
 0.14709534347960432,
 0.18653342966651124,
 0.255547304135755,
 0.2072330810137991,
 0.08952167274048284,
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#
#
#

#









#

#
#
#


# Verificando o coef_
clf2.coef_
# print clf2.coef_

# e o intercept
clf2.intercept_
#print(clf2.intercept_)

# Verificando o coeficiente angular
w1 = clf2.coef_[0][0]
print('O coeficiente angular é', w1)

# E o coeficiente linear
w0 = clf2.intercept_[0]
print('O coeficiente linear é', w0)

O coeficiente angular é 0.24501134423048582
O coeficiente linear é -1.2413081399442563


# Importando o expit
from scipy.special import expit

# Calculando o valor para cada valor de x e y
valores_x = np.linspace(X_train.min(),X_train.max(),100)
valores_y = expit(w1*valores_x+w0)

# Podemos exibir os dados de treino em cima dessa curva
y_curva = expit(w1*X_train+w0)
print(y_curva)

print(y_curva.shape)

[[0.19424782 0.19075467 0.15797471 ... 0.19111513 0.23267685 0.21596136]
 [0.39016257 0.27297767 0.26579497 ... 0.19111513 0.23267685 0.21596136]
 [0.23529712 0.25273044 0.18934511 ... 0.28022437 0.23267685 0.21596136]
 ...
 [0.21405734 0.20386926 0.26579497 ... 0.28022437 0.23267685 0.21596136]
 [0.28198528 0.26536054 0.18934511 ... 0.28022437 0.23267685 0.21596136]
 [0.23529712 0.19435886 0.15797471 ... 0.19111513 0.23267685 0.21596136]]
(699, 61)

Descrição do problema¶

Atributos do banco de dados¶

A Regressão Logística¶

Análise Exploratória¶

Análise Descritiva¶

No arquivo acima temos as mesmas informações, mas segmentadas por variável com valores binários¶

#¶

#¶

Podemos observar que agora cada variável categórica possui uma coluna própria, com 0 ou 1, indicando ausência ou presença.¶

Iremos pegar esse conjunto de dados (dummies) para prever o target (dados_y)¶

Ou seja, quanto mais perto a probabilidade de 1 , maior a chance de ser bom pagador¶

Valores com probabilidade acima de 0.5 são classificados como bons pagadores¶

Valores com probabilidade abaixo de 0.5 são classificados como maus pagadores¶

Outra forma de fazer...¶

	checking_account	duration_month	credit_history	purpose	credit_amount	savings_account	employed_since	installment_rate	status_sex	other_debtors	...	property	age	other_installment	housing	number_existing_credits	job	number_people_provide_maintenance	telephone	foreign_worker	response_variable
0	A12	48	A32	A43	5951	A61	A73	2	A92	A101	...	A121	22	A143	A152	1	A173	1	A191	A201	2
1	A14	12	A34	A46	2096	A61	A74	2	A93	A101	...	A121	49	A143	A152	1	A172	2	A191	A201	1
2	A11	42	A32	A42	7882	A61	A74	2	A93	A103	...	A122	45	A143	A153	1	A173	2	A191	A201	1
3	A11	24	A33	A40	4870	A61	A73	3	A93	A101	...	A124	53	A143	A153	2	A173	2	A191	A201	2
4	A14	36	A32	A46	9055	A65	A73	2	A93	A101	...	A124	35	A143	A153	1	A172	2	A192	A201	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
994	A14	12	A32	A42	1736	A61	A74	3	A92	A101	...	A121	31	A143	A152	1	A172	1	A191	A201	1
995	A11	30	A32	A41	3857	A61	A73	4	A91	A101	...	A122	40	A143	A152	1	A174	1	A192	A201	1
996	A14	12	A32	A43	804	A61	A75	4	A93	A101	...	A123	38	A143	A152	1	A173	1	A191	A201	1
997	A11	45	A32	A43	1845	A61	A73	4	A93	A101	...	A124	23	A143	A153	1	A173	1	A192	A201	2
998	A12	45	A34	A41	4576	A62	A71	3	A93	A101	...	A123	27	A143	A152	1	A173	1	A191	A201	1